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Improving Power System Risk Evaluation Method Using Monte Carlo Simulation and Gaussian Mixture MethodMOUSAVI, O. A. , FARASHBASHI-ASTANEH, M. S. , GHAREHPETIAN, G. B.
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Monte Carlo Simulation, Gaussian Mixture Method, linear programming, DC load flow, probability density function
power(18), systems(12), system(10), dobson(8), failure(7), cascading(7), model(6), blackouts(6), reliability(5), methods(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2009-06-02
Volume 9, Issue 2, Year 2009, On page(s): 38 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2009.02007
Web of Science Accession Number: 000268723600007
SCOPUS ID: 70349167393
The analysis of the risk of partial and total blackouts has a crucial role to determine safe limits in power system design, operation and upgrade. Due to huge cost of blackouts, it is very important to improve risk assessment methods. In this paper, Monte Carlo simulation (MCS) was used to analyze the risk and Gaussian Mixture Method (GMM) has been used to estimate the probability density function (PDF) of the load curtailment, in order to improve the power system risk assessment method. In this improved method, PDF and a suggested index have been used to analyze the risk of loss of load. The effect of considering the number of generation units of power plants in the risk analysis has been studied too. The improved risk assessment method has been applied to IEEE 118 bus and the network of Khorasan Regional Electric Company (KREC) and the PDF of the load curtailment has been determined for both systems. The effect of various network loadings, transmission unavailability, transmission capacity and generation unavailability conditions on blackout risk has been investigated too.
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